Model deployment is crucial for applying machine learning in realworld scenarios from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Model deployment is the process of making your trained model available for use in the real world. Without deployment, your model is essentially useless, as it remains confined to the development environment where it was created. Deploying a model involves integrating it into a production system where it can receive input data, make predictions, and provide output to end users. In real-world scenarios, the ultimate goal of machine learning is to solve practical problems and deliver value. Model deployment is crucial for achieving this goal, as it allows the model to be put into action and generate predictions that can be used to make informed decisions. For example, a deployed model could be used to predict customer c...
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